Estimation of regional crop yield by assimilating multi-temporal TM images into crop growth model Peng YANG
Qingbo Zhou, Zhongxin Chen, Yan Zha
Center for Climate System Research The University of Tokyo Chiba 277-8568, Japan
[email protected]
Key Laboratory of Resources Remote Sensing & Digital Agriculture, Ministry of Agriculture Institute of Agricultural Resources & Regional Planning, Chinese Academy of Agricultural Sciences Beijing 100081, China
Wenbin Wu, Ryosuke Shibasaki Center for Spatial Information Science The University of Tokyo Chiba 277-8568, Japan Abstract—This study presents the research result on estimating winter wheat yield in North China Plain, by assimilating multitemporal Landsat TM images into the GIS-based EPIC model (Erosion Productivity Impact Calculator, recently renamed Environmental Policy Integrated Climate). The results indicate that the assimilation of LAI maps retrieved from remotely sensed data has improved the yield simulation accuracy of GIS-based crop growth model at the regional scale significantly. Keywords-crop yield; data assimilation; leaf area index (LAI); landsat TM; crop growth model; winter wheat; North China Plain
I. INTRODUCTION Accurate and timely estimates or prediction of crop production in regional scale is critical for many applications such as food security warning system, agricultural lands management, food trade policy and carbon cycle research. Process-based crop growth models have been used successfully for simulating the physiological development, growth and yield of a crop at the field level on the basis of the interaction between environmental variables and plant physiological processes. These models require numerous model parameters and inputs on climatic conditions, soil characteristics, management practices and crop variables, which are the main limiting factors for the regional yield application. Remotely sensed data provide the spatial and temporal information of land surface at various scales, and are an attractive tool for assessment of the magnitude and variation of crop condition parameters. The integration of remotely sensed data with a crop growth model represents an important research direction in Precision Farming. In this study, the crop model EPIC from the U.S. Department of Agriculture (USDA) was combined with Geographical Information System (GIS) and multi-temporal Landsat TM images. The compound system was applied to simulate the winter wheat yield of year 2004 in North China Plain (NCP). The objective is to develop a framework for
scaling-up crop yield simulation from field level to regional scale, and to test the applicability of remote sensing data as a means of adjusting model parameters in the regional level. II.
STUDY AREA
The study was taken in North China Plain, which is one of the most important grain production bases of China and accounts for about 50% of wheat (Triticum aestivum L.) and 35% of maize (Zea mays L.) production in China. The study area (Fig. 1) is located in Hebei province consisting of 28 counties, and covers 114°15´E-115°40´E longitude by 36°30´N -38°20´N latitude and about 21470 km2. The land use in this area is dominated by the intensive dual-cropping system based on winter wheat and summer crops, including maize, soybean and cotton. The study area lies in a temperate semi-arid and semi-humid monsoon climate, with mean annual precipitation 550 mm. The dominant soil type is loam, with plenty of organic matter.
Figure 1. Location of the study area (gray area) in Hebei province. The black dots are the field measurement sites. The rectangle means the cover area of TM image (Path 124/Row 34).
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RS
EPIC Multi-temporal TM
EPIC model
Geometric correction Field observation data
Calibration & validation
Radiometric correction
GIS GIS-based EPIC model
Atmospheric correction RS+GIS+EPIC
Yield Retrieved LAI
Simulated LAI Estimation of regional crop yield
Lookup table method Optimizing algorithm
Sensitivity analysis County level census yields
Recalibrating model parameters
Figure 2. Flowchart of assimilating remotely sensed data into GIS-based EPIC model
III. MATERIALS AND METHODS The methodology for assimilating remotely sensed data into GIS-based EPIC model is briefly illustrated in Fig. 2. A. Field Measured LAI The LAI measurements were only taken in the fields of winter wheat. There was 85-field measurement sites evenly located in the 28 counties of study area (Fig. 1). The field measurements for each site are fixed on one 30m × 30m plot, where LAI were measured at five to nine subplots by using the standard, direct harvest method from Oct. 2003 to June 2004. Destructive samples began shortly after the emergence of winter wheat and continued through the milk stages. The leaf areas of the samples from subplots were averaged and combined with the plant density to get estimates of LAI for each plot. B. Remotely Sensed LAI During the growing season of winter wheat in 2004, there were only three cloud-free TM images (Path 124/Row 34) available for this study, acquired on March 7th, April 8th and June 11th, 2004. The geometric correction and radiometric correction were initially applied on the three TM image. Then, we performed a further atmospheric correction by using the atmospheric code 6S with the standard model parameter [1]. Regression analysis has been a popular empirical method of modeling the relationship between spectral data and LAI [2, 3]. Five of the most common vegetation indexes (RVI, NDVI, DVI, RDVI and MSAVI2) were selected for LAI estimation. The Curve Estimation procedure (SPSS for Windows, SPSS Inc., 2001) was used to estimate the relationship between field-measured LAI in 85 sites and TM
images acquired on March 7th and April 8th, with spectral VIs as the independent variable. The investigated models include linear, logarithmic, quadratic, cubic, power, inverse and exponential model. We validated these models by comparing differences in the coefficient of adjusted R2 and root mean square error (RMSE). C. EPIC Model Compared with many other process-based crop growth models, EPIC Model seems to be more suitable to simulate crop yields for relative comparisons of soils, crops, and management scenarios and has a good accuracy to estimate field yields [4]. EPIC Model was originally developed by USDA to examine the relationship between soil erosion and agricultural productivity [5, 6]. EPIC has been subjected to numerous validation exercises. Extensive tests of EPIC simulations were conducted at over 150 sites and on more than 10 crop species in all over the world and generally those tests concluded that EPIC adequately simulated crop yields. D. Integrating GIS with EPIC Model In this study, the loose coupling approach was used to integrate GIS with the EPIC. This approach uses two different packages directly. One is a standard GIS package (Arcview GIS 3.2) and another is EPIC program (EPIC version 8120). They are integrated by combining various data layers on the physical aspects of agricultural environments, via data exchange using either ASCII or binary data format between these two packages. The advantage of this approach is that redundant programming can be avoided. Map input, data handling, spatial analysis, and map output capabilities of GIS are used for the preparation of the land resource database required by the EPIC. The EPIC processing is outside of the GIS [4].
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E. Assimilating RS Data into GIS-based EPIC Model The assimilation approach minimizes the difference between values from remote sensing and the predicted ones from crop models by adjusting model parameters (recalibration) or the initial variables (re-initialization) [7, 8]. The estimated variable (LAI maps) from multi-temporal Landsat TM images is assimilated into the GIS-based EPIC model in this study by the Optimization algorithm and Lookup Table method. The optimization algorithm is used to minimize the merit function F(Ψ) [9], which looks like: F (Ψ ) =
n
(1)
∑ w [R − f (Ψ)]
2
i
i
i
i =1
Where f(Ψ) is the crop growth model with the parameter set Φ and w is the weighting vector. In this study, the objective function F(Ψ) is the minimum difference of LAI between values from multi-temporal remotely sensed data and the simulated LAI by GIS-based EPIC model. The set of unknowns or variables are the EPIC model parameters, which are decided by sensitivity analysis, such as DLAI, DMLA, DLP1 & DLP2 and RLAD [10]. The major limitation associated with the traditional optimization inversion approach (Iterative Techniques) is that it is very computationally expensive and therefore difficult to apply operationally for regional and global. The Lookup Table method was used to overcome the huge demand of computational time of the traditional optimization inversion method. IV.
RESULTS AND DISCUSSION
A. LAI Maps Retrieved from TM Images The field sites within TM 124/34 (85 sites) and two TM images (March 7th and April 8th) were considered together for analyzing the relationship between TM spectral vegetation indices and leaf area index of winter wheat in North China Plain. The image on June 11th, 2004 was not used for the regression analysis, because winter wheat in most of field sites had been harvested on that day. The regression relationships tended to be highly significant for each vegetation indices, used in this study. The adjusted R2 of the best regression model for each pair of SVI and LAI were higher than 0.70. This indicated that much of the variation occurring in LAI of winter wheat could be explained by using the spectral VIs from multi-temporal TM images. The exponential model gave a better fit than the linear model or other non-linear models for most of SVIs. Only the RVI-LAI got the best fitting equation by power model. The general relationships between SVI and LAI are curvilinear, as also reported by other authors [11, 12]. Finally, according to the results of statistic analysis, it was decided to construct maps of LAI using the exponential relationship between surface-reflectance-derived DVI and field-measured LAI.
B. Yield Simulation without RS Data Assimilation The yield simulation results were validated with the county census yield data, which were provided by the Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences (IARRP CAAS), Beijing, China. These county yield statistics, to some extent, represent a regional “ground truth”, and offer a validation of the prediction. Fig. 3 showed the relationships between the statistic yield and the simulated yield by GIS-based EPIC model in county level. The variability of county-level statistic yield shows a wide range, varied from 3.53 T/ha to 7.54 T/ha, due to the heterogeneity of soil, water resources, climatic conditions, and field management levels over the study area. However, the county-level yield simulated by GIS-based EPIC only varied from 5.37 T/ha to 6.32 T/ha. The correlation coefficient R2 was very low (0.119) (Fig. 3), which indicated that the simulated yield result by GIS-based EPIC could not describe the variability of true yield correctly. To a great extent, the low spatial resolution of input data (climate data, soil data and management data) for GIS-based EPIC model, especially soil characteristics, should be the main reason for the low simulation accuracy. Because the similar soil characteristics and climate conditions, the same management operation (irrigation schedule, fertilizer schedule and tillage schedule) and the same crop variables have been inputted into the GIS-based EPIC model. The homogenous yield distribution is outputted undoubtedly. If the spatial resolution and accuracy of the input can be improved, it is believed that the accuracy of the simulated yield by GIS-based EPIC model could be better [13]. C. Yield Simulation with RS Data Assimilation Fig. 4 showed the relationships between the statistic yield and the simulated yield by GIS-based EPIC model with RS data assimilation in county level. The county-level yield simulated by GIS-based EPIC with the assimilation data from remotely sensed images varied from 4.58 T/ha to 6.45 T/ha (Fig. 4). The average county-level yield for these 28 counties is 5.45 T/ha. The correlation coefficient R2 (0.5063) was significantly higher than GIS-based EPIC (0.119) without RS data assimilation, which indicated that the yield simulation ability of GIS-based EPIC have been improved by assimilating multi-temporal LAI maps and recalibrating the model parameters. Due to the cloud effect, it is difficult to obtain enough TM images during the key stages of crop growth, which limit the assimilation of multi-temporal remotely sensed data into GIS-based crop growth model. For this study, only two TM images with clear sky are available for the integration. However, the impact on yield simulation by assimilation of RS data is significant. The further research should be focused on using MODIS images with a higher temporal resolution and a wider spatial area, and on using a radiative transfer model to link the satellite data and crop growth model.
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assimilating multi-temporal remotely sensed data into GIS-based crop growth model.
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Simulated yield （ T/ha)
•
7 6 5
y = 0.0826x + 5.4025 R2 = 0.119 Statistic yield （ T/ha）
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The simulated yield from GIS-based EPIC model was improved significantly after assimilating two LAI maps of crop key stages into model, which has been proved by the application of the combination system in North China Plain in 2004. ACKNOWLEDGMENT
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Figure 3. The cross plots of county-level statistic yield and simulated yield from GIS-based EPIC for Winter wheat in 2004 8
Simulated yield （ T/ha) 7
Dr. G. X. Tan, Professor, Huazhong Normal University, is acknowledged for his critical assistance in developing GIS-based EPIC model. This research was supported by the National High Technology Research and Development Program of China (2003AA131020), and by the Core Research for Evolutional Science and Technology (CREST) in Japan (Modeling global hydrological cycles and world water resources coupled with human activities).
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REFERENCES 5
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y = 0.3315x + 3.4934 R2 = 0.5063 Statistic yield （ T/ha）
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Figure 4. The cross plots of county-level statistic yield and simulated yield from GIS-based EPIC with RS assimilation for Winter wheat in 2004 [3]
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CONCLUSIONS
Remotely sensed data provide the spatial and temporal information of land surface at various scales, and are an attractive tool for assessment of the magnitude and variation of crop condition parameters. The integration of remotely sensed data with a crop growth model represents an important research direction in Precision Farming. Integrated with Geographical Information System, physiology-based crop growth model in field level can be upscaled to the regional level. In this study, EPIC model (version 8120) was successfully integrated with GIS by the loose coupling approach. Data were exchanged using the ASCII or binary data format between GIS and EPIC model without a common user interface. The spatial resolution and accuracy of input on climatic conditions, soil characteristics, management practices and crop variables has a significant impact on the yield simulation result of GIS-based crop growth model. With the homogenous input, the simulated yield by GIS-based EPIC model only has the ability to explain 11.9% of the true yield variability in county-level in 2004. By using the Lookup table method and representative values for key variables, traditional optimization inversion approach successfully and efficiently applied in
the the was the
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